mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-30 13:24:35 +00:00
381efbf480
* wip llava python bindings compatibility * add external llava API * add base64 in-prompt image support * wip refactor image loading * refactor image load out of llava init * cleanup * further cleanup; move llava-cli into its own file and rename * move base64.hpp into common/ * collapse clip and llava libraries * move llava into its own subdir * wip * fix bug where base64 string was not removed from the prompt * get libllava to output in the right place * expose llava methods in libllama.dylib * cleanup memory usage around clip_image_* * cleanup and refactor *again* * update headerdoc * build with cmake, not tested (WIP) * Editorconfig * Editorconfig * Build with make * Build with make * Fix cyclical depts on Windows * attempt to fix build on Windows * attempt to fix build on Windows * Upd TODOs * attempt to fix build on Windows+CUDA * Revert changes in cmake * Fix according to review comments * Support building as a shared library * address review comments --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
57 lines
1.7 KiB
Markdown
57 lines
1.7 KiB
Markdown
# LLaVA
|
|
|
|
Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants.
|
|
|
|
The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)
|
|
and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)
|
|
models are available.
|
|
|
|
After API is confirmed, more models will be supported / uploaded.
|
|
|
|
## Usage
|
|
Build with cmake or run `make llava-cli` to build it.
|
|
|
|
After building, run: `./llava-cli` to see the usage. For example:
|
|
|
|
```sh
|
|
./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
|
|
```
|
|
|
|
**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
|
|
|
|
## Model conversion
|
|
|
|
- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally:
|
|
|
|
```sh
|
|
git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
|
|
|
|
git clone https://huggingface.co/openai/clip-vit-large-patch14-336
|
|
```
|
|
|
|
2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
|
|
|
|
```sh
|
|
python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
|
|
```
|
|
|
|
3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
|
|
|
|
```sh
|
|
python ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
|
|
```
|
|
|
|
4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
|
|
|
|
```sh
|
|
python ./convert.py ../llava-v1.5-7b
|
|
```
|
|
|
|
Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
|
|
|
|
## TODO
|
|
|
|
- [ ] Support non-CPU backend for the image encoding part.
|
|
- [ ] Support different sampling methods.
|
|
- [ ] Support more model variants.
|